Consolidation, Expansion Complicate Terminology Challenges

There are many integration challenges that arise in healthcare as organizations grow and evolve. For healthcare IT executives, it’s not just a matter of hard-wiring systems and installing interfaces – it’s ensuring that terminology is consistent and useful to clinicians and others that use the data.

Not everyone understands the technical challenges involved in working toward a consistent terminology across an organization. It can involve a substantial investment of time, effort and money.

A recent roundtable discussion hosted by Health Data Management and sponsored by Health Language discussed terminology and standards challenges that now face all segments of the industry.

FRED BAZZOLI: How are you dealing with the complexity of terminology? Do your peers understand the challenges of dealing with this problem?

JASON WOLFSON: Most of our clients come to us after many years and say, “We're in this project doing a data warehouse, and I hired a coding person, and they had a spreadsheet. They said it would take 2 months to do some mapping. Then it's 3 years later, and now there's ten people doing this. I just give up.”

SHEILA BRITNEY: I manage two data warehouses that have been running for about 10 to 15 years. Whatever our core systems are, we kind of coalesce the data together. We're a little bit ahead on this. We have code mappers and cross walkers and we have user interfaces for business units to maintain those. We have armies of analysts all over the company. One of the biggest hurdles of the data warehouse is engaging the analysts to put that data back into the data warehouse so the work isn't siloed.

AMY KNOPP: At Mayo, we have multiple departmental systems and multiple electronic health records. And we're bringing all of that data together and working on standardizing and really making good, effective use of data. There are standards, lots of them, and they are regularly updated. Our challenge is really with local codes and terminologies. There's hundreds of thousands of lab tests and orders in our order catalog, and we're bringing those for multiple applications. But they're not updating just once a quarter or once a month; they're updating every day. So if you're bringing in new lab tests, you're bringing in a new version of LOINC, and you need to look to see what lab tests might be impacted.

We've moved into managing groups of codes; now, we're managing hundreds of diseases and diagnoses and groups of procedures and groups of medications. We centralize the effort.

BAZZOLI: It sounds like everyone is collecting data. How can you tell if it’s being used in the right way? Who's using it? How are they using it? What are they trying to do with it?

STEVEN CHRISTOFF: That’s the payoff, where we can begin to see evidence of change. Are we truly getting in front of the cost curve? That's what the whole goal is.

DIANE CHRISTOPHERSON: Once you've gotten to the point where your data has some reliability, then you can identify populations, stratify populations, use predictive analytics, and then measure whatever intervention that you put in place, and whether or not it was effective. That's the end goal, and that's how you sell the importance of having good terminology and good data. But you have to find people who actually care about the terminology, find physicians who want to do this kind of work where they're looking at individual codes across lab and pharmacy, and EMR data and claims data and saying yup, this really has a correlation to a particular disease.

BAZZOLI: Some organizations are still struggling to put in electronic health record systems. This seems to be like a second-level, third-level discussion beyond just the implementation of electronic health records.

CHRISTOFF: Data is only data until it becomes information. Information is only information until it becomes intelligence. And intelligence is only intelligence until it becomes actionable. How do we move data down the path so it becomes actionable? There's a lot of steps to take, and a lot of different platforms, a lot of different languages, a lot of different utilizations. We need to get healthcare information into the hands of the people who are going to use it in the right way so that it makes sense to them and so they can effect change.

BRIAN LEVY: If we do our job correctly, the end user docs and nurses shouldn't be worried about choosing the right SNOMED code. They should be able to worry about being able to document what the patient has. The other challenge is how do you provide that value back to the doc? It takes more time to enter information into the EHR than it does to write it in the chart. But in terms of these broader uses of data for analytics, the extra work doesn’t provide additional value to me as an everyday doc.

BRITNEY: For a few years, our insurance arm has helped a large group of case managers by providing data through a group that kicks back risk scores that identify some potential customers or members that we need to target. They've done a lot with chronic disease items for case managers. It's enabling them with data and patient information to go help our members.

PAUL TUTEN: We definitely have interventions that reach back out to the attributed providers for patients. In the programs where we see the greatest movement of the needle, the docs are actually paid to take action; I mean directly to take action based upon the information.